Nothing fancy, but to get a handle of semantic segmentation methods, I re-implemented some well known models with a clear structured code (following this PyTorch template), in particularly:

• The implemented models are: Deeplab V3+ – GCN – PSPnet – Unet – Segnet and FCN

• Supported datasets: Pascal Voc, Cityscapes, ADE20K, COCO stuff,

• Losses: Dice-Loss, CE Dice loss, Focal Loss and Lovasz Softmax,

with various data augmentations and learning rate schedulers (poly learning rate and one cycle).

I though I share this implementation in case anyone might be interested, and here it is :

Github: https://github.com/yassouali/pytorch_segmentation